SOURCE-LINKED INTELLIGENCE
EvoSCM: Scientific Belief Revision Through Causal Model Evolution and Experimentation
Scientific agents must learn not only how to reason, but also what to believe. However, existing LLM agents typically express scientific hypotheses in free-form text, leaving their beliefs implicit and difficult to test or revise. We introduce EvoSCM, which equips scientific agents with explicit structural causal models that evolve as new experimental evidence is collected. EvoSCM maintains a population of competing SCM hypotheses, each encoding a candidate causal explanation of the environment, and evolves them through a closed discovery loop. In each round, the agent abduces latent mechanism
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Evidence & attribution
- arXiv · AI, language, vision and robotics · 2026-09-01T16:55:56.000Z
First collected: 2026-09-21T06:01:56.170Z. This is not the publication date.